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    "# Session 1: Introduction to Tensorflow\n",
    "<p class='lead'>\n",
    "Creative Applications of Deep Learning with Tensorflow<br />\n",
    "Parag K. Mital<br />\n",
    "Kadenze, Inc.<br />\n",
    "</p>\n",
    "\n",
    "<a name=\"learning-goals\"></a>\n",
    "# Learning Goals\n",
    "\n",
    "* Learn the basic idea behind machine learning: learning from data and discovering representations\n",
    "* Learn how to preprocess a dataset using its mean and standard deviation\n",
    "* Learn the basic components of a Tensorflow Graph\n",
    "\n",
    "# Table of Contents\n",
    "<!-- MarkdownTOC autolink=true autoanchor=true bracket=round -->\n",
    "\n",
    "- [Introduction](#introduction)\n",
    "    - [Promo](#promo)\n",
    "    - [Session Overview](#session-overview)\n",
    "- [Learning From Data](#learning-from-data)\n",
    "    - [Deep Learning vs. Machine Learning](#deep-learning-vs-machine-learning)\n",
    "    - [Invariances](#invariances)\n",
    "    - [Scope of Learning](#scope-of-learning)\n",
    "    - [Existing datasets](#existing-datasets)\n",
    "- [Preprocessing Data](#preprocessing-data)\n",
    "    - [Understanding Image Shapes](#understanding-image-shapes)\n",
    "    - [The Batch Dimension](#the-batch-dimension)\n",
    "    - [Mean/Deviation of Images](#meandeviation-of-images)\n",
    "    - [Dataset Preprocessing](#dataset-preprocessing)\n",
    "    - [Histograms](#histograms)\n",
    "    - [Histogram Equalization](#histogram-equalization)\n",
    "- [Tensorflow Basics](#tensorflow-basics)\n",
    "    - [Variables](#variables)\n",
    "    - [Tensors](#tensors)\n",
    "    - [Graphs](#graphs)\n",
    "    - [Operations](#operations)\n",
    "    - [Tensor](#tensor)\n",
    "    - [Sessions](#sessions)\n",
    "    - [Tensor Shapes](#tensor-shapes)\n",
    "    - [Many Operations](#many-operations)\n",
    "- [Convolution](#convolution)\n",
    "    - [Creating a 2-D Gaussian Kernel](#creating-a-2-d-gaussian-kernel)\n",
    "    - [Convolving an Image with a Gaussian](#convolving-an-image-with-a-gaussian)\n",
    "    - [Convolve/Filter an image using a Gaussian Kernel](#convolvefilter-an-image-using-a-gaussian-kernel)\n",
    "    - [Modulating the Gaussian with a Sine Wave to create Gabor Kernel](#modulating-the-gaussian-with-a-sine-wave-to-create-gabor-kernel)\n",
    "    - [Manipulating an image with this Gabor](#manipulating-an-image-with-this-gabor)\n",
    "- [Homework](#homework)\n",
    "- [Next Session](#next-session)\n",
    "- [Reading Material](#reading-material)\n",
    "\n",
    "<!-- /MarkdownTOC -->\n",
    "\n",
    "<a name=\"introduction\"></a>\n",
    "# Introduction\n",
    "\n",
    "This course introduces you to deep learning: the state-of-the-art approach to building artificial intelligence algorithms.  We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks.  A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications.  We'll see how to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image.  Deep learning offers enormous potential for creative applications and in this course we interrogate what's possible.  Through practical applications and guided homework assignments, you'll be expected to create datasets, develop and train neural networks, explore your own media collections using existing state-of-the-art deep nets, synthesize new content from generative algorithms, and understand deep learning's potential for creating entirely new aesthetics and new ways of interacting with large amounts of data.​​\n",
    "\n",
    "<a name=\"promo\"></a>\n",
    "## Promo\n",
    "\n",
    "Deep learning has emerged at the forefront of nearly every major computational breakthrough in the last 4 years.  It is no wonder that it is already in many of the products we use today, from netflix or amazon's personalized recommendations; to the filters that block our spam; to ways that we interact with personal assistants like Apple's Siri or Microsoft Cortana, even to the very ways our personal health is monitored.  And sure deep learning algorithms are capable of some amazing things.  But it's not just science applications that are benefiting from this research.\n",
    "\n",
    "Artists too are starting to explore how Deep Learning can be used in their own practice.  Photographers are starting to explore different ways of exploring visual media.  Generative artists are writing algorithms to create entirely new aesthetics. Filmmakers are exploring virtual worlds ripe with potential for procedural content.\n",
    "\n",
    "In this course, we're going straight to the state of the art.  And we're going to learn it all.  We'll see how to make an algorithm paint an image, or hallucinate objects in a photograph.  We'll see how to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets to using them to self organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of other images.  We'll even see how to teach a computer to read and synthesize new phrases.\n",
    "\n",
    "But we won't just be using other peoples code to do all of this.  We're going to develop everything ourselves using Tensorflow and I'm going to show you how to do it.  This course isn't just for artists nor is it just for programmers.  It's for people that want to learn more about how to apply deep learning with a hands on approach, straight into the python console, and learn what it all means through creative thinking and interaction.\n",
    "\n",
    "I'm Parag Mital, artist, researcher and Director of Machine Intelligence at Kadenze.  For the last 10 years, I've been exploring creative uses of computational models making use of machine and deep learning, film datasets, eye-tracking, EEG, and fMRI recordings exploring applications such as generative film experiences, augmented reality hallucinations, and expressive control of large audiovisual corpora.\n",
    "\n",
    "But this course isn't just about me.  It's about bringing all of you together.  It's about bringing together different backgrounds, different practices, and sticking all of you in the same virtual room, giving you access to state of the art methods in deep learning, some really amazing stuff, and then letting you go wild on the Kadenze platform.  We've been working very hard to build a platform for learning that rivals anything else out there for learning this stuff.\n",
    "\n",
    "You'll be able to share your content, upload videos, comment and exchange code and ideas, all led by the course I've developed for us.  But before we get there we're going to have to cover a lot of groundwork.  The basics that we'll use to develop state of the art algorithms in deep learning.  And that's really so we can better interrogate what's possible, ask the bigger questions, and be able to explore just where all this is heading in more depth.  With all of that in mind, Let's get started>\n",
    "\n",
    "Join me as we learn all about Creative Applications of Deep Learning with Tensorflow.\n",
    "\n",
    "<a name=\"session-overview\"></a>\n",
    "## Session Overview\n",
    "\n",
    "We're first going to talk about Deep Learning, what it is, and how it relates to other branches of learning.  We'll then talk about the major components of Deep Learning, the importance of datasets, and the nature of representation, which is at the heart of deep learning.\n",
    "\n",
    "If you've never used Python before, we'll be jumping straight into using libraries like numpy, matplotlib, and scipy. Before starting this session, please check the resources section for a notebook introducing some fundamentals of python programming.  When you feel comfortable with loading images from a directory, resizing, cropping, how to change an image datatype from unsigned int to float32, and what the range of each data type should be, then come back here and pick up where you left off.  We'll then get our hands dirty with Tensorflow, Google's library for machine intelligence.  We'll learn the basic components of creating a computational graph with Tensorflow, including how to convolve an image to detect interesting features at different scales.  This groundwork will finally lead us towards automatically learning our handcrafted features/algorithms.\n",
    "\n",
    "<a name=\"learning-from-data\"></a>\n",
    "# Learning From Data\n",
    "\n",
    "<a name=\"deep-learning-vs-machine-learning\"></a>\n",
    "## Deep Learning vs. Machine Learning\n",
    "\n",
    "So what is this word I keep using, Deep Learning.  And how is it different to Machine Learning?  Well Deep Learning is a *type* of Machine Learning algorithm that uses Neural Networks to learn.  The type of learning is \"Deep\" because it is composed of many layers of Neural Networks.  In this course we're really going to focus on supervised and unsupervised Deep Learning.  But there are many other incredibly valuable branches of Machine Learning such as Reinforcement Learning, Dictionary Learning, Probabilistic Graphical Models and Bayesian Methods (Bishop), or Genetic and Evolutionary Algorithms.  And any of these branches could certainly even be combined with each other or with Deep Networks as well.  We won't really be able to get into these other branches of learning in this course.  Instead, we'll focus more on building \"networks\", short for neural networks, and how they can do some really amazing things.  Before we can get into all that, we're going to need to understand a bit more about data and its importance in deep learning.\n",
    "\n",
    "<a name=\"invariances\"></a>\n",
    "## Invariances\n",
    "\n",
    "Deep Learning requires data.  A lot of it.  It's really one of the major reasons as to why Deep Learning has been so successful.  Having many examples of the thing we are trying to learn is the first thing you'll need before even thinking about Deep Learning.  Often, it is the biggest blocker to learning about something in the world.  Even as a child, we need a lot of experience with something before we begin to understand it.  I find I spend most of my time just finding the right data for a network to learn.  Getting it from various sources, making sure it all looks right and is labeled.  That is a lot of work.  The rest of it is easy as we'll see by the end of this course.\n",
    "\n",
    "Let's say we would like build a network that is capable of looking at an image and saying what object is in the image.  There are so many possible ways that an object could be manifested in an image.  It's rare to ever see just a single object in isolation. In order to teach a computer about an object, we would have to be able to give it an image of an object in every possible way that it could exist.\n",
    "\n",
    "We generally call these ways of existing \"invariances\".  That just means we are trying not to vary based on some factor.  We are invariant to it.  For instance, an object could appear to one side of an image, or another.  We call that translation invariance.  Or it could be from one angle or another.  That's called rotation invariance.  Or it could be closer to the camera, or farther.  and That would be scale invariance.  There are plenty of other types of invariances, such as perspective or brightness or exposure to give a few more examples for photographic images.\n",
    "\n",
    "<a name=\"scope-of-learning\"></a>\n",
    "## Scope of Learning\n",
    "\n",
    "With Deep Learning, you will always need a dataset that will teach the algorithm about the world.  But you aren't really teaching it everything.  You are only teaching it what is in your dataset!  That is a very important distinction.  If I show my algorithm only faces of people which are always placed in the center of an image, it will not be able to understand anything about faces that are not in the center of the image!  Well at least that's mostly true.\n",
    "\n",
    "That's not to say that a network is incapable of transfering what it has learned to learn new concepts more easily.  Or to learn things that might be necessary for it to learn other representations.  For instance, a network that has been trained to learn about birds, probably knows a good bit about trees, branches, and other bird-like hangouts, depending on the dataset.  But, in general, we are limited to learning what our dataset has access to.\n",
    "\n",
    "So if you're thinking about creating a dataset, you're going to have to think about what it is that you want to teach your network.  What sort of images will it see?  What representations do you think your network could learn given the data you've shown it?\n",
    "\n",
    "One of the major contributions to the success of Deep Learning algorithms is the amount of data out there.  Datasets have grown from orders of hundreds to thousands to many millions.   The more data you have, the more capable your network will be at determining whatever its objective is.\n",
    "\n",
    "<a name=\"existing-datasets\"></a>\n",
    "## Existing datasets\n",
    "\n",
    "With that in mind, let's try to find a dataset that we can work with.  There are a ton of datasets out there that current machine learning researchers use.  For instance if I do a quick Google search for Deep Learning Datasets, i can see for instance a link on deeplearning.net, listing a few interesting ones e.g. http://deeplearning.net/datasets/, including MNIST, CalTech, CelebNet, LFW, CIFAR, MS Coco, Illustration2Vec, and there are ton more.  And these are primarily image based.  But if you are interested in finding more, just do a quick search or drop a quick message on the forums if you're looking for something in particular.\n",
    "\n",
    "* MNIST\n",
    "* CalTech\n",
    "* CelebNet\n",
    "* ImageNet: http://www.image-net.org/\n",
    "* LFW\n",
    "* CIFAR10\n",
    "* CIFAR100\n",
    "* MS Coco: http://mscoco.org/home/\n",
    "* WLFDB: http://wlfdb.stevenhoi.com/\n",
    "* Flickr 8k: http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html\n",
    "* Flickr 30k\n",
    "\n",
    "<a name=\"preprocessing-data\"></a>\n",
    "# Preprocessing Data\n",
    "\n",
    "In this section, we're going to learn a bit about working with an image based dataset.  We'll see how image dimensions are formatted as a single image and how they're represented as a collection using a 4-d array.  We'll then look at how we can perform dataset normalization.  If you're comfortable with all of this, please feel free to skip to the next video.\n",
    "\n",
    "We're first going to load some libraries that we'll be making use of."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll be using a popular image dataset for faces called the CelebFaces dataset.  I've provided some helper functions which you can find on the resources page, which will just help us with manipulating images and loading this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from libs import utils\n",
    "# utils.<tab>\n",
    "files = utils.get_celeb_files()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the 50th image in this list of files, and then read the file at that location as an image, setting the result to a variable, `img`, and inspect a bit further what's going on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[178 185 191]\n",
      "  [182 189 195]\n",
      "  [149 156 162]\n",
      "  ..., \n",
      "  [216 220 231]\n",
      "  [220 224 233]\n",
      "  [220 224 233]]\n",
      "\n",
      " [[177 184 190]\n",
      "  [182 189 195]\n",
      "  [153 160 166]\n",
      "  ..., \n",
      "  [215 219 230]\n",
      "  [220 224 233]\n",
      "  [220 224 233]]\n",
      "\n",
      " [[177 184 190]\n",
      "  [182 189 195]\n",
      "  [161 168 174]\n",
      "  ..., \n",
      "  [214 218 229]\n",
      "  [219 223 232]\n",
      "  [219 223 232]]\n",
      "\n",
      " ..., \n",
      " [[ 11  14  23]\n",
      "  [ 11  14  23]\n",
      "  [ 11  14  23]\n",
      "  ..., \n",
      "  [  4   7  16]\n",
      "  [  5   8  17]\n",
      "  [  5   8  17]]\n",
      "\n",
      " [[  9  12  21]\n",
      "  [  9  12  21]\n",
      "  [  9  12  21]\n",
      "  ..., \n",
      "  [  8  11  18]\n",
      "  [  5   8  17]\n",
      "  [  5   8  17]]\n",
      "\n",
      " [[  9  12  21]\n",
      "  [  9  12  21]\n",
      "  [  9  12  21]\n",
      "  ..., \n",
      "  [  8  11  18]\n",
      "  [  5   8  17]\n",
      "  [  5   8  17]]]\n"
     ]
    }
   ],
   "source": [
    "img = plt.imread(files[50])\n",
    "# img.<tab>\n",
    "print(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When I print out this image, I can see all the numbers that represent this image.  We can use the function `imshow` to see this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1147f9320>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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31jg6Pn+LsPNIBNvOl2jyBMBHVCNO83aldt1YnA9IFVOiEO3eCd4iREBpxXw2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r63HS3j3x04XyZS3+81KPuG2wU4AuzSxqodx09e/GIppyhTV0O0Gmcld0/r0epCyUoUBTPCKMBA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\n5CtQICP8H2UN+SVHe7lrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a26d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If nothing is drawn and you are using notebook, try uncommenting the next line:\n",
    "#%matplotlib inline\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"understanding-image-shapes\"></a>\n",
    "## Understanding Image Shapes\n",
    "\n",
    "Let's break this data down a bit more.  We can see the dimensions of the data using the `shape` accessor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218, 178, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape\n",
    "# (218, 178, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that the image has 218 rows, 178 columns, and 3 color channels corresponding to the Red, Green, and Blue channels of the image, or RGB.  Let's try looking at just one of the color channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1148638d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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utYcolUqGVrKhUKPRQKlUmtJwzElkrK7X66HZbCKZTE51J2s0Ghb7K5VK6HQ6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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148c8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img[:, :, 0], cmap='gray')\n",
    "plt.imshow(img[:, :, 1], cmap='gray')\n",
    "plt.imshow(img[:, :, 2], cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the special colon operator to say take every value in this dimension.  This is saying, give me every row, every column, and the 0th dimension of the color channels.  What we're seeing is the amount of Red, Green, or Blue contributing to the overall color image.\n",
    "\n",
    "Let's use another helper function which will load every image file in the celeb dataset rather than just give us the filenames like before.  By default, this will just return the first 1000 images because loading the entire dataset is a bit cumbersome.  In one of the later sessions, I'll show you how tensorflow can handle loading images using a pipeline so we can load this same dataset.  For now, let's stick with this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgs = utils.get_celeb_imgs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a list containing our images.  Each index of the `imgs` list is another image which we can access using the square brackets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1151abba8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZsLKyQp7nKKWI4xilFN3dXayzbO/u0Ov1UFqjlCIKI/I8JwwjgPnEYa2jKEq0Dmg2Wxhj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2147b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imgs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"the-batch-dimension\"></a>\n",
    "## The Batch Dimension\n",
    "\n",
    "Remember that an image has a shape describing the height, width, channels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218, 178, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out we'll often use another convention for storing many images in an array using a new dimension called the batch dimension.  The resulting image shape will be exactly the same, except we'll stick on a new dimension on the beginning... giving us number of images x the height x the width x the number of color channels.\n",
    "\n",
    "N x H x W x C\n",
    "\n",
    "A Color image should have 3 color channels, RGB.\n",
    "\n",
    "We can combine all of our images to have these 4 dimensions by telling numpy to give us an array of all the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 218, 178, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array(imgs)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will only work if every image in our list is exactly the same size.  So if you have a wide image, short image, long image, forget about it.  You'll need them all to be the same size.  If you are unsure of how to get all of your images into the same size, then please please refer to the online resources for the notebook I've provided which shows you exactly how to take a bunch of images of different sizes, and crop and resize them the best we can to make them all the same size.\n",
    "\n",
    "<a name=\"meandeviation-of-images\"></a>\n",
    "## Mean/Deviation of Images\n",
    "\n",
    "Now that we have our data in a single numpy variable, we can do alot of cool stuff.  Let's look at the mean of the batch channel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x115387fd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3ZxaIzcK9fUAmDOY4zAIfK7SeqhGrSS0wJ7m3xfMKmAngBBiE7XZda800Ylds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\nPMybWdCKK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0vBx9RbhDQtWiHQbn8yBFe4sJGMdhTM4b16hS/ErL0/hDeQj+e/AYAdyPQX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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11516bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_img = np.mean(data, axis=0)\n",
    "plt.imshow(mean_img.astype(np.uint8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the first step towards building our robot overlords.  We've reduced down our entire dataset to a single representation which describes what most of our dataset looks like.  There is one other very useful statistic which we can look at very easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x115f1ae80>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LQj7ddzMImBKYfzThS7FxM912Ma8jdUu+0pe//GX84A/+IADggx/8IL70pS99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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115364748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "std_img = np.std(data, axis=0)\n",
    "plt.imshow(std_img.astype(np.uint8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this is incredibly cool.  We've just shown where changes are likely to be in our dataset of images.  Or put another way, we're showing where and how much variance there is in our previous mean image representation.\n",
    "\n",
    "We're looking at this per color channel.  So we'll see variance for each color channel represented separately, and then combined as a color image.  We can try to look at the average variance over all color channels by taking their mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11bab7c18>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/bqnXtaX8kqBx94SZZ3k3N3rtBVDLxG9udbOWmV7zSr4v0GNNK1rdDiaJvg5K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\ncGfAVILrXI0DhlXVSZGrrKC+Ubt04HGVQNAUVBn+ek0G0mE8G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CAX1O8QbiRP39lwwCQZ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\nLL5NbOKE4v8BGP/unAixCqsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1152d27b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.mean(std_img, axis=2).astype(np.uint8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is showing us on average, how every color channel will vary as a heatmap.  The more red, the more likely that our mean image is not the best representation.  The more blue, the less likely that our mean image is far off from any other possible image.\n",
    "\n",
    "<a name=\"dataset-preprocessing\"></a>\n",
    "## Dataset Preprocessing\n",
    "\n",
    "Think back to when I described what we're trying to accomplish when we build a model for machine learning?  We're trying to build a model that understands invariances.  We need our model to be able to express *all* of the things that can possibly change in our data.  Well, this is the first step in understanding what can change.  If we are looking to use deep learning to learn something complex about our data, it will often start by modeling both the mean and standard deviation of our dataset.  We can help speed things up by \"preprocessing\" our dataset by removing the mean and standard deviation.  What does this mean?  Subtracting the mean, and dividing by the standard deviation.  Another word for that is \"normalization\".\n",
    "\n",
    "<a name=\"histograms\"></a>\n",
    "## Histograms\n",
    "\n",
    "Let's have a look at our dataset another way to see why this might be a useful thing to do.  We're first going to convert our `batch` x `height` x `width` x `channels` array into a 1 dimensional array.  Instead of having 4 dimensions, we'll now just have 1 dimension of every pixel value stretched out in a long vector, or 1 dimensional array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[253 231 194]\n",
      "   [253 231 194]\n",
      "   [253 231 194]\n",
      "   ..., \n",
      "   [247 226 225]\n",
      "   [254 238 222]\n",
      "   [254 238 222]]\n",
      "\n",
      "  [[253 231 194]\n",
      "   [253 231 194]\n",
      "   [253 231 194]\n",
      "   ..., \n",
      "   [249 228 225]\n",
      "   [254 238 222]\n",
      "   [254 238 222]]\n",
      "\n",
      "  [[253 231 194]\n",
      "   [253 231 194]\n",
      "   [253 231 194]\n",
      "   ..., \n",
      "   [250 231 227]\n",
      "   [255 239 223]\n",
      "   [255 239 223]]\n",
      "\n",
      "  ..., \n",
      "  [[140  74  26]\n",
      "   [116  48   1]\n",
      "   [146  78  33]\n",
      "   ..., \n",
      "   [122  55  28]\n",
      "   [122  56  30]\n",
      "   [122  56  30]]\n",
      "\n",
      "  [[130  62  15]\n",
      "   [138  70  23]\n",
      "   [166  98  53]\n",
      "   ..., \n",
      "   [118  49  20]\n",
      "   [118  51  24]\n",
      "   [118  51  24]]\n",
      "\n",
      "  [[168 100  53]\n",
      "   [204 136  89]\n",
      "   [245 177 132]\n",
      "   ..., \n",
      "   [118  49  20]\n",
      "   [120  50  24]\n",
      "   [120  50  24]]]]\n",
      "[253 231 194 253 231 194 253 231 194 253]\n"
     ]
    }
   ],
   "source": [
    "flattened = data.ravel()\n",
    "print(data[:1])\n",
    "print(flattened[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first convert our N x H x W x C dimensional array into a 1 dimensional array.  The values of this array will be based on the last dimensions order.  So we'll have: [<font color='red'>251</font>, <font color='green'>238</font>, <font color='blue'>205</font>, <font color='red'>251</font>, <font color='green'>238</font>, <font color='blue'>206</font>, <font color='red'>253</font>, <font color='green'>240</font>, <font color='blue'>207</font>, ...]\n",
    "\n",
    "We can visualize what the \"distribution\", or range and frequency of possible values are.  This is a very useful thing to know.  It tells us whether our data is predictable or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 216804.,  117821.,   99125.,   71049.,   66478.,   62066.,\n",
       "          62528.,   58085.,   56686.,   56114.,   56848.,   58551.,\n",
       "          63168.,   61086.,   59193.,   59305.,   62526.,   63596.,\n",
       "          62285.,   65061.,   63389.,   61989.,   61411.,   60742.,\n",
       "          60464.,   60307.,   59074.,   59312.,   60353.,   64807.,\n",
       "          67305.,   61667.,   59906.,   60546.,   62017.,   62268.,\n",
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       "          57822.,   61451.,   63481.,   57782.,   57212.,   56516.,\n",
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       "          51012.,   48826.,   47602.,   46064.,   45351.,   43735.,\n",
       "          42849.,   42903.,   42571.,   41789.,   42412.,   42705.,\n",
       "          42982.,   43107.,   43372.,   43416.,   43323.,   42808.,\n",
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       "          41885.,   42650.,   41703.,   42498.,   41983.,   42702.,\n",
       "          42735.,   43540.,   42428.,   42376.,   42394.,   41547.,\n",
       "          42188.,   41566.,   40389.,   41123.,   40529.,   41291.,\n",
       "          42112.,   41069.,   41266.,   41475.,   41254.,   41075.,\n",
       "          41171.,   41432.,   41208.,   41324.,   41922.,   41582.,\n",
       "          42013.,   42234.,   42460.,   42677.,   42786.,   41815.,\n",
       "          41975.,   42119.,   41887.,   41585.,   41084.,   40260.,\n",
       "          40819.,   40261.,   40525.,   40688.,   40924.,   41340.,\n",
       "          42065.,   41707.,   42271.,   41754.,   42675.,   42349.,\n",
       "          43355.,   44380.,   44117.,   43332.,   42148.,   41166.,\n",
       "          41503.,   41165.,   40511.,   40550.,   40868.,   40656.,\n",
       "          40710.,   40901.,   41201.,   41412.,   41278.,   41196.,\n",
       "          42040.,   40484.,   41872.,   40003.,   39540.,   39412.,\n",
       "          39419.,   39000.,   39120.,   41259.,   40142.,   39750.,\n",
       "          39523.,   39662.,   40226.,   40471.,   40539.,   41334.,\n",
       "          40587.,   39869.,   41072.,   42369.,   38908.,   36865.,\n",
       "          36743.,   37041.,   37979.,   37332.,   36199.,   37241.,\n",
       "          38136.,   38718.,   40154.,   40233.,   40927.,   42931.,\n",
       "          42440.,   44739.,   43546.,   47023.,   45519.,   43844.,\n",
       "          39307.,   38330.,   38893.,   39873.,   38623.,   39838.,\n",
       "          40250.,   40881.,   39237.,   39683.,   38854.,   38480.,\n",
       "          40170.,   40491.,   41457.,   39982.,   38166.,   37335.,\n",
       "          36543.,   37362.,   36063.,   37213.,   38517.,   39497.,\n",
       "          39859.,   37243.,   38053.,   36426.,   32394.,   37110.,\n",
       "          32811.,   31258.,   27632.,   35700.,   30117.,   29352.,\n",
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       "          23938.,   22960.,   24523.,   23963.,   28714.,   30620.,\n",
       "          30354.,   41291.,  149885.]),\n",
       " array([   0.,    1.,    2.,    3.,    4.,    5.,    6.,    7.,    8.,\n",
       "           9.,   10.,   11.,   12.,   13.,   14.,   15.,   16.,   17.,\n",
       "          18.,   19.,   20.,   21.,   22.,   23.,   24.,   25.,   26.,\n",
       "          27.,   28.,   29.,   30.,   31.,   32.,   33.,   34.,   35.,\n",
       "          36.,   37.,   38.,   39.,   40.,   41.,   42.,   43.,   44.,\n",
       "          45.,   46.,   47.,   48.,   49.,   50.,   51.,   52.,   53.,\n",
       "          54.,   55.,   56.,   57.,   58.,   59.,   60.,   61.,   62.,\n",
       "          63.,   64.,   65.,   66.,   67.,   68.,   69.,   70.,   71.,\n",
       "          72.,   73.,   74.,   75.,   76.,   77.,   78.,   79.,   80.,\n",
       "          81.,   82.,   83.,   84.,   85.,   86.,   87.,   88.,   89.,\n",
       "          90.,   91.,   92.,   93.,   94.,   95.,   96.,   97.,   98.,\n",
       "          99.,  100.,  101.,  102.,  103.,  104.,  105.,  106.,  107.,\n",
       "         108.,  109.,  110.,  111.,  112.,  113.,  114.,  115.,  116.,\n",
       "         117.,  118.,  119.,  120.,  121.,  122.,  123.,  124.,  125.,\n",
       "         126.,  127.,  128.,  129.,  130.,  131.,  132.,  133.,  134.,\n",
       "         135.,  136.,  137.,  138.,  139.,  140.,  141.,  142.,  143.,\n",
       "         144.,  145.,  146.,  147.,  148.,  149.,  150.,  151.,  152.,\n",
       "         153.,  154.,  155.,  156.,  157.,  158.,  159.,  160.,  161.,\n",
       "         162.,  163.,  164.,  165.,  166.,  167.,  168.,  169.,  170.,\n",
       "         171.,  172.,  173.,  174.,  175.,  176.,  177.,  178.,  179.,\n",
       "         180.,  181.,  182.,  183.,  184.,  185.,  186.,  187.,  188.,\n",
       "         189.,  190.,  191.,  192.,  193.,  194.,  195.,  196.,  197.,\n",
       "         198.,  199.,  200.,  201.,  202.,  203.,  204.,  205.,  206.,\n",
       "         207.,  208.,  209.,  210.,  211.,  212.,  213.,  214.,  215.,\n",
       "         216.,  217.,  218.,  219.,  220.,  221.,  222.,  223.,  224.,\n",
       "         225.,  226.,  227.,  228.,  229.,  230.,  231.,  232.,  233.,\n",
       "         234.,  235.,  236.,  237.,  238.,  239.,  240.,  241.,  242.,\n",
       "         243.,  244.,  245.,  246.,  247.,  248.,  249.,  250.,  251.,\n",
       "         252.,  253.,  254.,  255.]),\n",
       " <a list of 255 Patch objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KlcbfABERMWfEnsoTTzzBjh07Bi07deoUjz76KMXFxQQCAeeDv6GhgaqqKoqKiti+fTul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\njEJFRESM+X+nJKRhAaiTzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f31e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(flattened.ravel(), 255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last line is saying give me a histogram of every value in the vector, and use 255 bins.  Each bin is grouping a range of values.  The bars of each bin describe the frequency, or how many times anything within that range of values appears.In other words, it is telling us if there is something that seems to happen more than anything else.  If there is, it is likely that a neural network will take advantage of that.\n",
    "\n",
    "\n",
    "<a name=\"histogram-equalization\"></a>\n",
    "## Histogram Equalization\n",
    "\n",
    "The mean of our dataset looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    2.,     0.,     0.,     0.,     2.,    18.,    30.,    23.,\n",
       "           42.,    42.,    43.,    41.,    51.,    48.,    30.,    52.,\n",
       "           58.,    59.,    67.,    67.,    71.,    83.,    94.,   110.,\n",
       "          110.,   107.,   134.,   103.,   109.,   160.,   198.,   206.,\n",
       "          211.,   265.,   306.,   289.,   344.,   335.,   361.,   382.,\n",
       "          402.,   390.,   400.,   497.,   544.,   594.,   663.,   679.,\n",
       "          749.,   803.,   847.,   882.,   886.,   938.,   817.,   857.,\n",
       "          801.,   825.,   831.,   896.,   875.,   861.,   843.,   870.,\n",
       "          842.,   897.,   863.,   861.,   906.,   939.,   905.,   935.,\n",
       "          946.,   972.,  1004.,  1064.,  1079.,  1124.,  1162.,  1103.,\n",
       "         1129.,  1108.,  1085.,  1079.,  1051.,  1155.,  1068.,  1093.,\n",
       "         1170.,  1171.,  1146.,  1213.,  1146.,  1174.,  1211.,  1229.,\n",
       "         1173.,  1196.,  1216.,  1150.,  1255.,  1373.,  1344.,  1445.,\n",
       "         1554.,  1554.,  1553.,  1478.,  1506.,  1469.,  1546.,  1626.,\n",
       "         1659.,  1568.,  1548.,  1559.,  1576.,  1376.,  1283.,  1189.,\n",
       "         1151.,  1093.,  1046.,  1068.,  1021.,  1059.,   985.,   927.,\n",
       "          801.,   696.,   566.,   508.,   448.,   399.,   352.,   371.,\n",
       "          343.,   345.,   321.,   285.,   293.,   283.,   261.,   244.,\n",
       "          218.,   221.,   263.,   210.,   208.,   202.,   191.,   173.,\n",
       "          209.,   182.,   208.,   199.,   196.,   185.,   197.,   172.,\n",
       "          188.,   147.,   157.,   174.,   147.,   168.,   138.,   140.,\n",
       "          130.,   102.,   104.,    99.,   117.,   103.,   113.,   112.,\n",
       "           86.,    93.,    84.,    77.,    95.,    88.,    89.,    72.,\n",
       "           64.,    71.,    63.,    58.,    56.,    52.,    60.,    57.,\n",
       "           55.,    59.,    57.,    54.,    66.,    60.,    65.,    72.,\n",
       "           67.,    75.,    83.,    91.,    84.,    83.,    89.,    78.,\n",
       "           83.,    85.,   102.,    82.,    77.,    89.,   111.,   101.,\n",
       "           89.,   100.,   111.,   109.,    97.,    83.,    80.,    72.,\n",
       "           85.,   108.,   108.,    91.,    80.,    83.,    82.,    86.,\n",
       "           96.,    98.,    77.,    73.,    74.,    64.,    63.,    56.,\n",
       "           49.,    62.,    50.,    52.,    64.,    64.,    64.,    77.,\n",
       "           50.,    33.,    26.,    30.,    10.,     8.,     7.]),\n",
       " array([  43.25      ,   43.95035294,   44.65070588,   45.35105882,\n",
       "          46.05141176,   46.75176471,   47.45211765,   48.15247059,\n",
       "          48.85282353,   49.55317647,   50.25352941,   50.95388235,\n",
       "          51.65423529,   52.35458824,   53.05494118,   53.75529412,\n",
       "          54.45564706,   55.156     ,   55.85635294,   56.55670588,\n",
       "          57.25705882,   57.95741176,   58.65776471,   59.35811765,\n",
       "          60.05847059,   60.75882353,   61.45917647,   62.15952941,\n",
       "          62.85988235,   63.56023529,   64.26058824,   64.96094118,\n",
       "          65.66129412,   66.36164706,   67.062     ,   67.76235294,\n",
       "          68.46270588,   69.16305882,   69.86341176,   70.56376471,\n",
       "          71.26411765,   71.96447059,   72.66482353,   73.36517647,\n",
       "          74.06552941,   74.76588235,   75.46623529,   76.16658824,\n",
       "          76.86694118,   77.56729412,   78.26764706,   78.968     ,\n",
       "          79.66835294,   80.36870588,   81.06905882,   81.76941176,\n",
       "          82.46976471,   83.17011765,   83.87047059,   84.57082353,\n",
       "          85.27117647,   85.97152941,   86.67188235,   87.37223529,\n",
       "          88.07258824,   88.77294118,   89.47329412,   90.17364706,\n",
       "          90.874     ,   91.57435294,   92.27470588,   92.97505882,\n",
       "          93.67541176,   94.37576471,   95.07611765,   95.77647059,\n",
       "          96.47682353,   97.17717647,   97.87752941,   98.57788235,\n",
       "          99.27823529,   99.97858824,  100.67894118,  101.37929412,\n",
       "         102.07964706,  102.78      ,  103.48035294,  104.18070588,\n",
       "         104.88105882,  105.58141176,  106.28176471,  106.98211765,\n",
       "         107.68247059,  108.38282353,  109.08317647,  109.78352941,\n",
       "         110.48388235,  111.18423529,  111.88458824,  112.58494118,\n",
       "         113.28529412,  113.98564706,  114.686     ,  115.38635294,\n",
       "         116.08670588,  116.78705882,  117.48741176,  118.18776471,\n",
       "         118.88811765,  119.58847059,  120.28882353,  120.98917647,\n",
       "         121.68952941,  122.38988235,  123.09023529,  123.79058824,\n",
       "         124.49094118,  125.19129412,  125.89164706,  126.592     ,\n",
       "         127.29235294,  127.99270588,  128.69305882,  129.39341176,\n",
       "         130.09376471,  130.79411765,  131.49447059,  132.19482353,\n",
       "         132.89517647,  133.59552941,  134.29588235,  134.99623529,\n",
       "         135.69658824,  136.39694118,  137.09729412,  137.79764706,\n",
       "         138.498     ,  139.19835294,  139.89870588,  140.59905882,\n",
       "         141.29941176,  141.99976471,  142.70011765,  143.40047059,\n",
       "         144.10082353,  144.80117647,  145.50152941,  146.20188235,\n",
       "         146.90223529,  147.60258824,  148.30294118,  149.00329412,\n",
       "         149.70364706,  150.404     ,  151.10435294,  151.80470588,\n",
       "         152.50505882,  153.20541176,  153.90576471,  154.60611765,\n",
       "         155.30647059,  156.00682353,  156.70717647,  157.40752941,\n",
       "         158.10788235,  158.80823529,  159.50858824,  160.20894118,\n",
       "         160.90929412,  161.60964706,  162.31      ,  163.01035294,\n",
       "         163.71070588,  164.41105882,  165.11141176,  165.81176471,\n",
       "         166.51211765,  167.21247059,  167.91282353,  168.61317647,\n",
       "         169.31352941,  170.01388235,  170.71423529,  171.41458824,\n",
       "         172.11494118,  172.81529412,  173.51564706,  174.216     ,\n",
       "         174.91635294,  175.61670588,  176.31705882,  177.01741176,\n",
       "         177.71776471,  178.41811765,  179.11847059,  179.81882353,\n",
       "         180.51917647,  181.21952941,  181.91988235,  182.62023529,\n",
       "         183.32058824,  184.02094118,  184.72129412,  185.42164706,\n",
       "         186.122     ,  186.82235294,  187.52270588,  188.22305882,\n",
       "         188.92341176,  189.62376471,  190.32411765,  191.02447059,\n",
       "         191.72482353,  192.42517647,  193.12552941,  193.82588235,\n",
       "         194.52623529,  195.22658824,  195.92694118,  196.62729412,\n",
       "         197.32764706,  198.028     ,  198.72835294,  199.42870588,\n",
       "         200.12905882,  200.82941176,  201.52976471,  202.23011765,\n",
       "         202.93047059,  203.63082353,  204.33117647,  205.03152941,\n",
       "         205.73188235,  206.43223529,  207.13258824,  207.83294118,\n",
       "         208.53329412,  209.23364706,  209.934     ,  210.63435294,\n",
       "         211.33470588,  212.03505882,  212.73541176,  213.43576471,\n",
       "         214.13611765,  214.83647059,  215.53682353,  216.23717647,\n",
       "         216.93752941,  217.63788235,  218.33823529,  219.03858824,\n",
       "         219.73894118,  220.43929412,  221.13964706,  221.84      ]),\n",
       " <a list of 255 Patch objects>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IuIvJ6/Wyfft2ANatW8fDDz9MLBajv7+fgwcPAnDo0CH6+voA6O/vp7m5GdM0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\nImkpQIiISFoKECIikpYChIiIpPX/AUYLAq5MJNRZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f3cf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(mean_img.ravel(), 255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we subtract an image by our mean image, we remove all of this information from it.  And that means that the rest of the information is really what is important for describing what is unique about it.\n",
    "\n",
    "Let's try and compare the histogram before and after \"normalizing our data\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11c7b80f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TP1fJ2fVRW+3Vtc9WG7q3bdumjz/+WOeff75+//vfy+Vy6ZZbbtENN9ygefPm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c3f1748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 20\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 6), sharey=True, sharex=True)\n",
    "axs[0].hist((data[0]).ravel(), bins)\n",
    "axs[0].set_title('img distribution')\n",
    "axs[1].hist((mean_img).ravel(), bins)\n",
    "axs[1].set_title('mean distribution')\n",
    "axs[2].hist((data[0] - mean_img).ravel(), bins)\n",
    "axs[2].set_title('(img - mean) distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we can see from the histograms is the original image's distribution of values from 0 - 255.  The mean image's data distribution is mostly centered around the value 100.  When we look at the difference of the original image and the mean image as a histogram, we can see that the distribution is now centered around 0.  What we are seeing is the distribution of values that were above the mean image's intensity, and which were below it.  Let's take it one step further and complete the normalization by dividing by the standard deviation of our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11cbd7780>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GDBhgYmJiTGBgoImLizOPPvqox0DwF154wcTGxpqAgACrfG9t8ja9+PWUKVNM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\nG2G9xg7kBgUqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb196d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(12, 6), sharey=True, sharex=True)\n",
    "axs[0].hist((data[0] - mean_img).ravel(), bins)\n",
    "axs[0].set_title('(img - mean) distribution')\n",
    "axs[1].hist((std_img).ravel(), bins)\n",
    "axs[1].set_title('std deviation distribution')\n",
    "axs[2].hist(((data[0] - mean_img) / std_img).ravel(), bins)\n",
    "axs[2].set_title('((img - mean) / std_dev) distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our data has been squished into a peak!  We'll have to look at it on a different scale to see what's going on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5, 5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axs[2].set_xlim([-150, 150])\n",
    "axs[2].set_xlim([-100, 100])\n",
    "axs[2].set_xlim([-50, 50])\n",
    "axs[2].set_xlim([-10, 10])\n",
    "axs[2].set_xlim([-5, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we can see is that the data is in the range of -3 to 3, with the bulk of the data centered around -1 to 1.  This is the effect of normalizing our data: most of the data will be around 0, where some deviations of it will follow between -3 to 3.\n",
    "\n",
    "If our data does not end up looking like this, then we should either (1): get much more data to calculate our mean/std deviation, or (2): either try another method of normalization, such as scaling the values between 0 to 1, or -1 to 1, or possibly not bother with normalization at all.  There are other options that one could explore, including different types of normalization such as local contrast normalization for images or PCA based normalization but we won't have time to get into those in this course.\n",
    "\n",
    "<a name=\"tensorflow-basics\"></a>\n",
    "# Tensorflow Basics\n",
    "\n",
    "Let's now switch gears and start working with Google's Library for Numerical Computation, TensorFlow.  This library can do most of the things we've done so far.  However, it has a very different approach for doing so.  And it can do a whole lot more cool stuff which we'll eventually get into.  The major difference to take away from the remainder of this session is that instead of computing things immediately, we first define things that we want to compute later using what's called a `Graph`.  Everything in Tensorflow takes place in a computational graph and running and evaluating anything in the graph requires a `Session`.  Let's take a look at how these both work and then we'll get into the benefits of why this is useful:\n",
    "\n",
    "<a name=\"variables\"></a>\n",
    "## Variables\n",
    "\n",
    "We're first going to import the tensorflow library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at how we might create a range of numbers.  Using numpy, we could for instance use the linear space function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.         -2.93939394 -2.87878788 -2.81818182 -2.75757576 -2.6969697\n",
      " -2.63636364 -2.57575758 -2.51515152 -2.45454545 -2.39393939 -2.33333333\n",
      " -2.27272727 -2.21212121 -2.15151515 -2.09090909 -2.03030303 -1.96969697\n",
      " -1.90909091 -1.84848485 -1.78787879 -1.72727273 -1.66666667 -1.60606061\n",
      " -1.54545455 -1.48484848 -1.42424242 -1.36363636 -1.3030303  -1.24242424\n",
      " -1.18181818 -1.12121212 -1.06060606 -1.         -0.93939394 -0.87878788\n",
      " -0.81818182 -0.75757576 -0.6969697  -0.63636364 -0.57575758 -0.51515152\n",
      " -0.45454545 -0.39393939 -0.33333333 -0.27272727 -0.21212121 -0.15151515\n",
      " -0.09090909 -0.03030303  0.03030303  0.09090909  0.15151515  0.21212121\n",
      "  0.27272727  0.33333333  0.39393939  0.45454545  0.51515152  0.57575758\n",
      "  0.63636364  0.6969697   0.75757576  0.81818182  0.87878788  0.93939394\n",
      "  1.          1.06060606  1.12121212  1.18181818  1.24242424  1.3030303\n",
      "  1.36363636  1.42424242  1.48484848  1.54545455  1.60606061  1.66666667\n",
      "  1.72727273  1.78787879  1.84848485  1.90909091  1.96969697  2.03030303\n",
      "  2.09090909  2.15151515  2.21212121  2.27272727  2.33333333  2.39393939\n",
      "  2.45454545  2.51515152  2.57575758  2.63636364  2.6969697   2.75757576\n",
      "  2.81818182  2.87878788  2.93939394  3.        ]\n",
      "(100,)\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "x = np.linspace(-3.0, 3.0, 100)\n",
    "\n",
    "# Immediately, the result is given to us.  An array of 100 numbers equally spaced from -3.0 to 3.0.\n",
    "print(x)\n",
    "\n",
    "# We know from numpy arrays that they have a `shape`, in this case a 1-dimensional array of 100 values\n",
    "print(x.shape)\n",
    "\n",
    "# and a `dtype`, in this case float64, or 64 bit floating point values.\n",
    "print(x.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"tensors\"></a>\n",
    "## Tensors\n",
    "\n",
    "In tensorflow, we could try to do the same thing using their linear space function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"LinSpace:0\", shape=(100,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "x = tf.linspace(-3.0, 3.0, 100)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of a `numpy.array`, we are returned a `tf.Tensor`.  The name of it is \"LinSpace:0\".  Wherever we see this colon 0, that just means the output of.  So the name of this Tensor is saying, the output of LinSpace.\n",
    "\n",
    "Think of `tf.Tensor`s the same way as you would the `numpy.array`.  It is described by its `shape`, in this case, only 1 dimension of 100 values.  And it has a `dtype`, in this case, `float32`.  But *unlike* the `numpy.array`, there are no values printed here!  That's because it actually hasn't computed its values yet.  Instead, it just refers to the output of a `tf.Operation` which has been already been added to Tensorflow's default computational graph.  The result of that operation is the tensor that we are returned.\n",
    "\n",
    "<a name=\"graphs\"></a>\n",
    "## Graphs\n",
    "\n",
    "Let's try and inspect the underlying graph.  We can request the \"default\" graph where all of our operations have been added:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"operations\"></a>\n",
    "## Operations\n",
    "\n",
    "And from this graph, we can get a list of all the operations that have been added, and print out their names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LinSpace/start', 'LinSpace/stop', 'LinSpace/num', 'LinSpace']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[op.name for op in g.get_operations()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So Tensorflow has named each of our operations to generally reflect what they are doing.  There are a few parameters that are all prefixed by LinSpace, and then the last one which is the operation which takes all of the parameters and creates an output for the linspace.\n",
    "\n",
    "<a name=\"tensor\"></a>\n",
    "## Tensor\n",
    "\n",
    "We can request the output of any operation, which is a tensor, by asking the graph for the tensor's name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'LinSpace:0' shape=(100,) dtype=float32>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.get_tensor_by_name('LinSpace' + ':0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What I've done is asked for the `tf.Tensor` that comes from the operation \"LinSpace\".  So remember, the result of a `tf.Operation` is a `tf.Tensor`.  Remember that was the same name as the tensor `x` we created before.\n",
    "\n",
    "<a name=\"sessions\"></a>\n",
    "## Sessions\n",
    "\n",
    "In order to actually compute anything in tensorflow, we need to create a `tf.Session`.  The session is responsible for evaluating the `tf.Graph`.  Let's see how this works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.         -2.939394   -2.87878799 -2.81818175 -2.75757575 -2.69696975\n",
      " -2.63636351 -2.5757575  -2.5151515  -2.4545455  -2.3939395  -2.33333325\n",
      " -2.27272725 -2.21212125 -2.15151501 -2.090909   -2.030303   -1.969697\n",
      " -1.90909088 -1.84848475 -1.78787875 -1.72727275 -1.66666663 -1.6060605\n",
      " -1.5454545  -1.4848485  -1.42424238 -1.36363626 -1.30303025 -1.24242425\n",
      " -1.18181813 -1.12121201 -1.060606   -1.         -0.939394   -0.87878776\n",
      " -0.81818175 -0.75757575 -0.69696951 -0.63636351 -0.5757575  -0.5151515\n",
      " -0.4545455  -0.39393926 -0.33333325 -0.27272725 -0.21212101 -0.15151501\n",
      " -0.090909   -0.030303    0.030303    0.09090924  0.15151525  0.21212125\n",
      "  0.27272749  0.33333349  0.3939395   0.4545455   0.5151515   0.57575774\n",
      "  0.63636374  0.69696975  0.75757599  0.81818199  0.87878799  0.939394    1.\n",
      "  1.060606    1.12121201  1.18181849  1.24242449  1.30303049  1.36363649\n",
      "  1.4242425   1.4848485   1.5454545   1.60606098  1.66666698  1.72727299\n",
      "  1.78787899  1.84848499  1.909091    1.969697    2.030303    2.090909\n",
      "  2.15151548  2.21212149  2.27272749  2.33333349  2.3939395   2.4545455\n",
      "  2.5151515   2.57575798  2.63636398  2.69696999  2.75757599  2.81818199\n",
      "  2.87878799  2.939394    3.        ]\n",
      "[-3.         -2.939394   -2.87878799 -2.81818175 -2.75757575 -2.69696975\n",
      " -2.63636351 -2.5757575  -2.5151515  -2.4545455  -2.3939395  -2.33333325\n",
      " -2.27272725 -2.21212125 -2.15151501 -2.090909   -2.030303   -1.969697\n",
      " -1.90909088 -1.84848475 -1.78787875 -1.72727275 -1.66666663 -1.6060605\n",
      " -1.5454545  -1.4848485  -1.42424238 -1.36363626 -1.30303025 -1.24242425\n",
      " -1.18181813 -1.12121201 -1.060606   -1.         -0.939394   -0.87878776\n",
      " -0.81818175 -0.75757575 -0.69696951 -0.63636351 -0.5757575  -0.5151515\n",
      " -0.4545455  -0.39393926 -0.33333325 -0.27272725 -0.21212101 -0.15151501\n",
      " -0.090909   -0.030303    0.030303    0.09090924  0.15151525  0.21212125\n",
      "  0.27272749  0.33333349  0.3939395   0.4545455   0.5151515   0.57575774\n",
      "  0.63636374  0.69696975  0.75757599  0.81818199  0.87878799  0.939394    1.\n",
      "  1.060606    1.12121201  1.18181849  1.24242449  1.30303049  1.36363649\n",
      "  1.4242425   1.4848485   1.5454545   1.60606098  1.66666698  1.72727299\n",
      "  1.78787899  1.84848499  1.909091    1.969697    2.030303    2.090909\n",
      "  2.15151548  2.21212149  2.27272749  2.33333349  2.3939395   2.4545455\n",
      "  2.5151515   2.57575798  2.63636398  2.69696999  2.75757599  2.81818199\n",
      "  2.87878799  2.939394    3.        ]\n"
     ]
    }
   ],
   "source": [
    "# We're first going to create a session:\n",
    "sess = tf.Session()\n",
    "\n",
    "# Now we tell our session to compute anything we've created in the tensorflow graph.\n",
    "computed_x = sess.run(x)\n",
    "print(computed_x)\n",
    "\n",
    "# Alternatively, we could tell the previous Tensor to evaluate itself using this session:\n",
    "computed_x = x.eval(session=sess)\n",
    "print(computed_x)\n",
    "\n",
    "# We can close the session after we're done like so:\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also explicitly tell the session which graph we want to manage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session(graph=g)\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, it grabs the default graph.  But we could have created a new graph like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g2 = tf.Graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then used this graph only in our session.\n",
    "\n",
    "To simplify things, since we'll be working in iPython's interactive console, we can create an `tf.InteractiveSession`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.        , -2.939394  , -2.87878799, -2.81818175, -2.75757575,\n",
       "       -2.69696975, -2.63636351, -2.5757575 , -2.5151515 , -2.4545455 ,\n",
       "       -2.3939395 , -2.33333325, -2.27272725, -2.21212125, -2.15151501,\n",
       "       -2.090909  , -2.030303  , -1.969697  , -1.90909088, -1.84848475,\n",
       "       -1.78787875, -1.72727275, -1.66666663, -1.6060605 , -1.5454545 ,\n",
       "       -1.4848485 , -1.42424238, -1.36363626, -1.30303025, -1.24242425,\n",
       "       -1.18181813, -1.12121201, -1.060606  , -1.        , -0.939394  ,\n",
       "       -0.87878776, -0.81818175, -0.75757575, -0.69696951, -0.63636351,\n",
       "       -0.5757575 , -0.5151515 , -0.4545455 , -0.39393926, -0.33333325,\n",
       "       -0.27272725, -0.21212101, -0.15151501, -0.090909  , -0.030303  ,\n",
       "        0.030303  ,  0.09090924,  0.15151525,  0.21212125,  0.27272749,\n",
       "        0.33333349,  0.3939395 ,  0.4545455 ,  0.5151515 ,  0.57575774,\n",
       "        0.63636374,  0.69696975,  0.75757599,  0.81818199,  0.87878799,\n",
       "        0.939394  ,  1.        ,  1.060606  ,  1.12121201,  1.18181849,\n",
       "        1.24242449,  1.30303049,  1.36363649,  1.4242425 ,  1.4848485 ,\n",
       "        1.5454545 ,  1.60606098,  1.66666698,  1.72727299,  1.78787899,\n",
       "        1.84848499,  1.909091  ,  1.969697  ,  2.030303  ,  2.090909  ,\n",
       "        2.15151548,  2.21212149,  2.27272749,  2.33333349,  2.3939395 ,\n",
       "        2.4545455 ,  2.5151515 ,  2.57575798,  2.63636398,  2.69696999,\n",
       "        2.75757599,  2.81818199,  2.87878799,  2.939394  ,  3.        ], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "x.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we didn't have to explicitly tell the `eval` function about our session.  We'll leave this session open for the rest of the lecture.\n",
    "\n",
    "<a name=\"tensor-shapes\"></a>\n",
    "## Tensor Shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n",
      "[100]\n"
     ]
    }
   ],
   "source": [
    "# We can find out the shape of a tensor like so:\n",
    "print(x.get_shape())\n",
    "\n",
    "# %% Or in a more friendly format\n",
    "print(x.get_shape().as_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"many-operations\"></a>\n",
    "## Many Operations\n",
    "\n",
    "Lets try a set of operations now.  We'll try to create a Gaussian curve.  This should resemble a normalized histogram where most of the data is centered around the mean of 0.  It's also sometimes refered to by the bell curve or normal curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The 1 dimensional gaussian takes two parameters, the mean value, and the standard deviation, which is commonly denoted by the name sigma.\n",
    "mean = 0.0\n",
    "sigma = 1.0\n",
    "\n",
    "# Don't worry about trying to learn or remember this formula.  I always have to refer to textbooks or check online for the exact formula.\n",
    "z = (tf.exp(tf.neg(tf.pow(x - mean, 2.0) /\n",
    "                   (2.0 * tf.pow(sigma, 2.0)))) *\n",
    "     (1.0 / (sigma * tf.sqrt(2.0 * 3.1415))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like before, amazingly, we haven't actually computed anything.  We *have just added a bunch of operations to Tensorflow's graph.  Whenever we want the value or output of this operation, we'll have to explicitly ask for the part of the graph we're interested in before we can see its result.  Since we've created an interactive session, we should just be able to say the name of the Tensor that we're interested in, and call the `eval` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11d64fbe0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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agmhxXHEWvG2zrL/cjMjbGzRiDHi97C2IhpGmIFocf/E5yHIjqG2I88HiqtGwm8HffAE+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\nuXPnQERYvXo1Xnr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c3f6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = z.eval()\n",
    "plt.plot(res)\n",
    "# if nothing is drawn, and you are using ipython notebook, uncomment the next two lines:\n",
    "#%matplotlib inline\n",
    "#plt.plot(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"convolution\"></a>\n",
    "# Convolution\n",
    "\n",
    "<a name=\"creating-a-2-d-gaussian-kernel\"></a>\n",
    "## Creating a 2-D Gaussian Kernel\n",
    "\n",
    "Let's try creating a 2-dimensional Gaussian.  This can be done by multiplying a vector by its transpose.  If you aren't familiar with matrix math, I'll review a few important concepts.  This is about 98% of what neural networks do so if you're unfamiliar with this, then please stick with me through this and it'll be smooth sailing.  First, to multiply two matrices, their inner dimensions must agree, and the resulting matrix will have the shape of the outer dimensions.\n",
    "\n",
    "So let's say we have two matrices, X and Y.  In order for us to multiply them, X's columns must match Y's rows.  I try to remember it like so:\n",
    "<pre>\n",
    "    (X_rows, X_cols) x (Y_rows, Y_cols)\n",
    "      |       |           |      |\n",
    "      |       |___________|      |\n",
    "      |             ^            |\n",
    "      |     inner dimensions     |\n",
    "      |        must match        |\n",
    "      |                          |\n",
    "      |__________________________|\n",
    "                    ^\n",
    "           resulting dimensions\n",
    "         of matrix multiplication\n",
    "</pre>\n",
    "But our matrix is actually a vector, or a 1 dimensional matrix.  That means its dimensions are N x 1.  So to multiply them, we'd have:\n",
    "<pre>\n",
    "     (N,      1)    x    (1,     N)\n",
    "      |       |           |      |\n",
    "      |       |___________|      |\n",
    "      |             ^            |\n",
    "      |     inner dimensions     |\n",
    "      |        must match        |\n",
    "      |                          |\n",
    "      |__________________________|\n",
    "                    ^\n",
    "           resulting dimensions\n",
    "         of matrix multiplication\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11d6c5d30>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ktNFkKlnxqY5YAFsh2WuOszwKEJQUXzrOLD6DVz43CPeNZkNWHmBSflrr0/9DlXoNENCh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c8907f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's store the number of values in our Gaussian curve.\n",
    "ksize = z.get_shape().as_list()[0]\n",
    "\n",
    "# Let's multiply the two to get a 2d gaussian\n",
    "z_2d = tf.matmul(tf.reshape(z, [ksize, 1]), tf.reshape(z, [1, ksize]))\n",
    "\n",
    "# Execute the graph\n",
    "plt.imshow(z_2d.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"convolving-an-image-with-a-gaussian\"></a>\n",
    "## Convolving an Image with a Gaussian\n",
    "\n",
    "A very common operation that we'll come across with Deep Learning is convolution.  We're going to explore what this means using our new gaussian kernel that we've just created.  For now, just think of it a way of filtering information.  We're going to effectively filter our image using this Gaussian function, as if the gaussian function is the lens through which we'll see our image data.  What it will do is at every location we tell it to filter, it will average the image values around it based on what the kernel's values are.  The Gaussian's kernel is basically saying, take a lot the center, a then decesasingly less as you go farther away from the center.  The effect of convolving the image with this type of kernel is that the entire image will be blurred.  If you would like an interactive exploratin of convolution, this website is great:\n",
    "\n",
    "http://setosa.io/ev/image-kernels/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7Htrb21FfX48NGzagpKQEALBp0ya8/PLLCIVCuP/++3HllVeOuzPBYNCT5FM6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\nzW9+g6effho9PT2e1QUFJWD02rsdh1Vk7a8qwvu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cHERfX58DEGvcCrjWIbFtC5j8nHM6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\nB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tGWdv0MjVLs7AvA8siB3zWxajRgMrjdb/r+WkBoo9VyNg5TyPA3ru49PT2yOmqDoVaB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c89db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's first load an image.  We're going to need a grayscale image to begin with.  skimage has some images we can play with.  If you do not have the skimage module, you can load your own image, or get skimage by pip installing \"scikit-image\".\n",
    "from skimage import data\n",
    "img = data.camera().astype(np.float32)\n",
    "plt.imshow(img, cmap='gray')\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice our img shape is 2-dimensional.  For image convolution in Tensorflow, we need our images to be 4 dimensional.  Remember that when we load many iamges and combine them in a single numpy array, the resulting shape has the number of images first.\n",
    "\n",
    "N x H x W x C\n",
    "\n",
    "In order to perform 2d convolution with tensorflow, we'll need the same dimensions for our image.  With just 1 grayscale image, this means the shape will be:\n",
    "\n",
    "1 x H x W x 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 512, 512, 1)\n",
      "Tensor(\"Reshape_2:0\", shape=(1, 512, 512, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# We could use the numpy reshape function to reshape our numpy array\n",
    "img_4d = img.reshape([1, img.shape[0], img.shape[1], 1])\n",
    "print(img_4d.shape)\n",
    "\n",
    "# but since we'll be using tensorflow, we can use the tensorflow reshape function:\n",
    "img_4d = tf.reshape(img, [1, img.shape[0], img.shape[1], 1])\n",
    "print(img_4d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of getting a numpy array back, we get a tensorflow tensor.  This means we can't access the `shape` parameter like we did with the numpy array.  But instead, we can use `get_shape()`, and `get_shape().as_list()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 512, 512, 1)\n",
      "[1, 512, 512, 1]\n"
     ]
    }
   ],
   "source": [
    "print(img_4d.get_shape())\n",
    "print(img_4d.get_shape().as_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The H x W image is now part of a 4 dimensional array, where the other dimensions of N and C are 1.  So there is only 1 image and only 1 channel.\n",
    "\n",
    "We'll also have to reshape our Gaussian Kernel to be 4-dimensional as well.  The dimensions for kernels are slightly different!  Remember that the image is:\n",
    "\n",
    "Number of Images x Image Height x Image Width x Number of Channels\n",
    "\n",
    "we have:\n",
    "\n",
    "Kernel Height x Kernel Width x Number of Input Channels x Number of Output Channels\n",
    "\n",
    "Our Kernel already has a height and width of `ksize` so we'll stick with that for now.  The number of input channels should match the number of channels on the image we want to convolve.  And for now, we just keep the same number of output channels as the input channels, but we'll later see how this comes into play."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 100, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "# Reshape the 2d kernel to tensorflow's required 4d format: H x W x I x O\n",
    "z_4d = tf.reshape(z_2d, [ksize, ksize, 1, 1])\n",
    "print(z_4d.get_shape().as_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"convolvefilter-an-image-using-a-gaussian-kernel\"></a>\n",
    "## Convolve/Filter an image using a Gaussian Kernel\n",
    "\n",
    "We can now use our previous Gaussian Kernel to convolve our image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 512, 512, 1)\n"
     ]
    }
   ],
   "source": [
    "convolved = tf.nn.conv2d(img_4d, z_4d, strides=[1, 1, 1, 1], padding='SAME')\n",
    "res = convolved.eval()\n",
    "print(res.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two new parameters here: `strides`, and `padding`.  Strides says how to move our kernel across the image.  Basically, we'll only ever use it for one of two sets of parameters:\n",
    "\n",
    "[1, 1, 1, 1], which means, we are going to convolve every single image, every pixel, and every color channel by whatever the kernel is.\n",
    "\n",
    "and the second option:\n",
    "\n",
    "[1, 2, 2, 1], which means, we are going to convolve every single image, but every other pixel, in every single color channel.\n",
    "\n",
    "Padding says what to do at the borders.  If we say \"SAME\", that means we want the same dimensions going in as we do going out.  In order to do this, zeros must be padded around the image.  If we say \"VALID\", that means no padding is used, and the image dimensions will actually change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11f661c50>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IDSDk52KkPgcwVlZWpNlsypUrV2RlZUWuXLliDrOmzsvAGArT75Brom4An8Nt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\nXFPqLwpdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f661240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib cannot handle plotting 4D images!  We'll have to convert this back to the original shape.  There are a few ways we could do this.  We could plot by \"squeezing\" the singleton dimensions.\n",
    "plt.imshow(np.squeeze(res), cmap='gray')\n",
    "\n",
    "# Or we could specify the exact dimensions we want to visualize:\n",
    "plt.imshow(res[0, :, :, 0], cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"modulating-the-gaussian-with-a-sine-wave-to-create-gabor-kernel\"></a>\n",
    "## Modulating the Gaussian with a Sine Wave to create Gabor Kernel\n",
    "\n",
    "We've now seen how to use tensorflow to create a set of operations which create a 2-dimensional Gaussian kernel, and how to use that kernel to filter or convolve another image.  Let's create another interesting convolution kernel called a Gabor.  This is a lot like the Gaussian kernel, except we use a sine wave to modulate that.\n",
    "\n",
    "<graphic: draw 1d gaussian wave, 1d sine, show modulation as multiplication and resulting gabor.>\n",
    "\n",
    "We first use linspace to get a set of values the same range as our gaussian, which should be from -3 standard deviations to +3 standard deviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xs = tf.linspace(-3.0, 3.0, ksize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then calculate the sine of these values, which should give us a nice wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116306208>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pwjmQ6KaQFm1MRyEKaCyH85CBw4s/4vhmu+koAUF3fwPdvgky+G+moxAFPJbD\neUjFyrCGj4E9Zxr0BD9ecifNz4P97muw7hgFqcLnABOZxnIoh1zTCnLt9dC5b5iO4td0/tuQBk0g\nrdqZjkJEYDlcELn1LujP/4O9dZ3pKH5Jv0yFfrMdMvQ+01GI6HcshwsgISGwRo6FznsLmn3IdBy/\noscOw35vGqy/juWKq0RehOVwgaReQ0iX3rBnT4Xatuk4fkFVYb87DdK+K6RRjOk4RHQalsNFkB6D\ngIIC6MqFpqP4BV23HDiSDenLB/gQeRuWw0WQoCBY9zwKXb0Iumen6Tg+TX/+H/STubDueRQSzLWT\niLwNy+EiiePy4stbZ74IPZFjOo5P0pO5sGc8DxlyD+SKuqbjENFZsBwugbRsC7muPex3pvLZDxdJ\nVaHv/RfSpAWsdp1MxyGic2A5XCIZOBw4ehi6KtF0FJ+i61ZAf/sFMnik6ShEdB4sh0skwZfBGjUO\nmvwJdGea6Tg+QX/YBV00F9Z9j0MqhJiOQ0TnwXJwgjhqwrrnUdizXoJmHjAdx6vpkSzYbzwH664H\neZ6ByAewHJwkjZtDegyEPX0yND/fdByvpAUFsKc/C+l0C6QlV1sl8gUsBxeQrn0hEVHQd1/lDXJ/\noqrF61KFhUN6xZuOQ0QXiOXgAiICGT4GmpUB/WSu6TheRZOToHt3w7r7HxAR03GI6AKxHFxEKoTA\nGv0kNPUz2BuSTcfxCrptA3T1YlgPPg2pWMl0HCK6CCwHF5JqYbAeeBq6cA702y9NxzFKv98Je+4M\nWA9MgDhqmo5DRBeJ5eBiUicS1n2Pw575AvSnH0zHMUIP7IP9xrOwRj4MufJq03GI6BKwHNxAGjeD\ndeco2K/9G7r/Z9NxPEozD8B++WnIwLsgMdeajkNEl4jl4CZyXXvIwBGwX/lXwNwDodmHYL80AXLL\nIFjtu5qOQ0ROYDm4kXVDHKTHINgvTYBmZ5qO41Z69HBxMcT1hBXX03QcInISy8HNrLiekLhesJ8f\nD834zXQct9DDWbBffArSrhOsmweYjkNELsBy8ADr5v6QWwbCfuFJ6G+/mI7jUprxG+znx0FuiIPV\nZ4jpOETkIiwHD7E694AMGAb7xaf85iom3fcj7CnjIbcMhNVjkOk4RORCwaYDBBLrhjhoSAjsV56B\nNeJBSMu2piNdMv3ua9hvToEMuQdWm46m4xCRi7EcPEyuaw8rLBz29MmQg79Cburvc8tK2CnLoIs+\nLH7EZ9ML8vUjAAAIZUlEQVSWpuMQkRvwYyUDpH5jWOOmQDevhc6ZBi04ZTrSBdHCQtjvvw5dsxTW\nuOdZDER+jOVgiITXhPX4c8DJXNj/94jXn6jWzAOwX3gCmn0I1vgpkFp1TEciIjdiORgkFStB7vsn\npGsf2M+Ph71+ldc9k1pVYW/6FPZ/HoXEdoA15ilIpcqmYxGRm/Gcg2EiAul4MzS6Kew3p0C3b4Z1\n+32QmleYjlZ8/8K8mcBv+2A9MhESyXWSiAIFZw5eQupcCevJFyGNmsH+zyOwl8yDFhQYyaKFBbBX\nLID97wchtesW52IxEAUUp2YOW7Zswfz587Fv3z5MnjwZ9evXP+t2aWlpmD17NlQVcXFx6N+/vzOH\n9VsSfBmkx0Bo246wP3wT+vT9kJ63QW7oAgl2/yRP7SLoto3QRR8Cter8fm4hwu3HJSLv49RPnKio\nKDz66KN48803z7mNbduYNWsWnn76adSoUQPjx49HmzZtULcuHzJ/LhJeC0FjnoLuToe95CPo0gRI\nz0HQrr3dcjwtKIBuWQtdsQCoFgZryN8gzVq75VhE5BucKoeIiPL/Vrlnzx7UqVMHNWsWP/ClQ4cO\nSE1NZTlcAGkUg6CHJ0L37IS9YiGOLZwDtGoHad8ViL4GYl36p4Jq28CendCt66DbNwFXRcO660FI\noxgXvgMi8lVu/6wiOzsb4eHhJa8dDgf27Nnj7sP6FYm+BkFjrkGVogLkrF4Ce+4M4Gg20Kg5pElz\nSIMmQK06kIrnvopIT+UDB/dD93xbXAq7vwGqhELadYb11MuQ8FoefEdE5O3KLYeJEyfi6NGjJa9V\nFSKCIUOGIDY21q3h6ExWmANW9wFA9wHQ7Ezod98A330FO2U5cOggUCEECK8FXFYBsAQQCzh5Asg+\nBOTlApfXhjRoClzTClbf2yG1eT6BiM6u3HKYMGGCUwdwOBw4dOhQyevs7Gw4HI5zbp+eno709PSS\n1/Hx8Rf08VWgCA0NLf5FRATQrCWAO4zmMaVkHIhjcRqORamEhISSX8fExCAm5uI+Mnb7pazR0dE4\ncOAAMjMzUVhYiI0bN553xhETE4P4+PiSf05/g4GOY1GM41CKY1GKY1EqISHhjJ+jF1sMgJPnHD7/\n/HO88847OHbsGJ599lnUq1cPTzzxBA4fPowZM2Zg3LhxsCwLI0eOxKRJk6Cq6NKlCyIjI505LBER\nuZlT5dC2bVu0bVt22ekaNWpg3LhxJa9btWqFqVOnOnMoIiLyIK+/Q/pSpkP+imNRjONQimNRimNR\nyhVjIeptK70REZFxXj9zICIiz2M5EBFRGV67ZHcgL9aXlZWFadOm4ejRoxARdO3aFT179sTx48fx\nyiuvIDMzE7Vq1cLYsWNRuXJgPFvBtm2MHz8eDocDjz/+eMCORW5uLt544w388ssvEBGMGjUKderU\nCcixSExMxPr162FZFqKionD//fcjLy8vIMZi+vTp2L59O6pXr44XXngBAM77/0RiYiLWrl2LoKAg\njBgxAi1bXsBTHNULFRUV6ZgxYzQjI0MLCgr00Ucf1X379pmO5TGHDx/WvXv3qqrqyZMn9cEHH9R9\n+/bpe++9p0lJSaqqmpiYqO+//77BlJ61ePFinTp1qj777LOqqgE7FtOmTdM1a9aoqmphYaGeOHEi\nIMciIyNDR48erQUFBaqq+tJLL+natWsDZiy+/fZb3bt3rz7yyCMlv3eu9/7LL7/oY489poWFhXrw\n4EEdM2aM2rZd7jG88mOl0xfrCw4OLlmsL1CEhYWhXr16AICKFSuibt26yMrKwrZt29CpUycAQOfO\nnQNmTLKysrBjxw507dq15PcCcSxyc3Oxa9cuxMXFAQCCgoJQuXLlgByLSpUqITg4GHl5eSgqKsKp\nU6fgcDgCZiyaNGmCKlWqnPF753rv27ZtQ/v27REUFIRatWqhTp06F7S+nVd+rMTF+kplZGTgp59+\nQqNGjXD06FGEhYUBKC6Q09e88mfvvvsuhg0bhtzc3JLfC8SxyMjIQGhoKF5//XX89NNPqF+/PkaM\nGBGQY1G1alX07t0b999/P0JCQtCiRQu0aNEiIMfiD+d679nZ2WjUqFHJdg6HA9nZ2eXuzytnDlQs\nLy8PL730EkaMGIGKFSuW+bqIGEjlWX98rlqvXr3zPl87EMbCtm3s3bsX3bt3x3PPPYeQkBAkJSWV\n2S4QxuLgwYNYunQpXn/9dcyYMQP5+flYv359me0CYSzOxdn37pUzh4tdrM8fFRUV4cUXX8SNN96I\nNm3aACj+28CRI0dK/l29enXDKd1v165d2LZtG3bs2IFTp07h5MmTeO211wJyLBwOB8LDw9GgQQMA\nwPXXX4+kpKSAHIsffvgBjRs3RtWqVQEUr9bw3XffBeRY/OFc7/3PP0+zsrIu6OepV84cLnaxPn80\nffp0REZGomfPniW/17p1a6SkpAAAUlJSAmJMbr/9dkyfPh3Tpk3DQw89hGbNmuGBBx4IyLEICwtD\neHg49u/fDwD4+uuvERkZGZBjERERge+//x6nTp2CqgbkWKjqGbPpc7332NhYbNq0CYWFhcjIyMCB\nAwcQHR1d7v699g7ptLQ0vPPOOyWL9QXSpay7du3CM888g6ioKIgIRARDhw5FdHQ0Xn75ZRw6dAg1\na9bE2LFjy5yU8mc7d+7E4sWLSy5lDcSx+PHHHzFjxgwUFhaidu3auP/++2HbdkCOxaJFi5CSkgLL\nslCvXj38/e9/R15eXkCMxdSpU7Fz507k5OSgevXqiI+PR5s2bc753hMTE7FmzRoEBwdf8KWsXlsO\nRERkjld+rERERGaxHIiIqAyWAxERlcFyICKiMlgORERUBsuBiIjKYDkQEVEZLAciIirj/wNuna+L\nJuaA/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ff17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ys = tf.sin(xs)\n",
    "plt.figure()\n",
    "plt.plot(ys.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for multiplication, we'll need to convert this 1-dimensional vector to a matrix: N x 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ys = tf.reshape(ys, [ksize, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then repeat this wave across the matrix by using a multiplication of ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11636d2b0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EIkGhUAgeBHPDwFOeJj/484LMli04CL7++mtOnDjBkSNHqK+v5+DBgwwPD99yu9vtP3K5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f614630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ones = tf.ones((1, ksize))\n",
    "wave = tf.matmul(ys, ones)\n",
    "plt.imshow(wave.eval(), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can directly multiply our old Gaussian kernel by this wave and get a gabor kernel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11645f6a0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ScwTiymgRgVWntCrjfM1PLaSX2pX6YfVTSmslOkII8Cqs2QDI0QNXBxclUDst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\nlfbZjXoTMTExUb/33nvrx44dq3/44Yf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d669cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gabor = tf.mul(wave, z_2d)\n",
    "plt.imshow(gabor.eval(), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"manipulating-an-image-with-this-gabor\"></a>\n",
    "## Manipulating an image with this Gabor\n",
    "\n",
    "We've already gone through the work of convolving an image.  The only thing that has changed is the kernel that we want to convolve with.  We could have made life easier by specifying in our graph which elements we wanted to be specified later.  Tensorflow calls these \"placeholders\", meaning, we're not sure what these are yet, but we know they'll fit in the graph like so, generally the input and output of the network.\n",
    "\n",
    "Let's rewrite our convolution operation using a placeholder for the image and the kernel and then see how the same operation could have been done.  We're going to set the image dimensions to `None` x `None`.  This is something special for placeholders which tells tensorflow \"let this dimension be any possible value\".  1, 5, 100, 1000, it doesn't matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, ?, 1)\n",
      "[1, None, None, 1]\n"
     ]
    }
   ],
   "source": [
    "# This is a placeholder which will become part of the tensorflow graph, but\n",
    "# which we have to later explicitly define whenever we run/evaluate the graph.\n",
    "# Pretty much everything you do in tensorflow can have a name.  If we don't\n",
    "# specify the name, tensorflow will give a default one, like \"Placeholder_0\".\n",
    "# Let's use a more useful name to help us understand what's happening.\n",
    "img = tf.placeholder(tf.float32, shape=[None, None], name='img')\n",
    "\n",
    "\n",
    "# We'll reshape the 2d image to a 3-d tensor just like before:\n",
    "# Except now we'll make use of another tensorflow function, expand dims, which adds a singleton dimension at the axis we specify.\n",
    "# We use it to reshape our H x W image to include a channel dimension of 1\n",
    "# our new dimensions will end up being: H x W x 1\n",
    "img_3d = tf.expand_dims(img, 2)\n",
    "dims = img_3d.get_shape()\n",
    "print(dims)\n",
    "\n",
    "# And again to get: 1 x H x W x 1\n",
    "img_4d = tf.expand_dims(img_3d, 0)\n",
    "print(img_4d.get_shape().as_list())\n",
    "\n",
    "# Let's create another set of placeholders for our Gabor's parameters:\n",
    "mean = tf.placeholder(tf.float32, name='mean')\n",
    "sigma = tf.placeholder(tf.float32, name='sigma')\n",
    "ksize = tf.placeholder(tf.int32, name='ksize')\n",
    "\n",
    "# Then finally redo the entire set of operations we've done to convolve our\n",
    "# image, except with our placeholders\n",
    "x = tf.linspace(-3.0, 3.0, ksize)\n",
    "z = (tf.exp(tf.neg(tf.pow(x - mean, 2.0) /\n",
    "                   (2.0 * tf.pow(sigma, 2.0)))) *\n",
    "      (1.0 / (sigma * tf.sqrt(2.0 * 3.1415))))\n",
    "z_2d = tf.matmul(\n",
    "  tf.reshape(z, tf.pack([ksize, 1])),\n",
    "  tf.reshape(z, tf.pack([1, ksize])))\n",
    "ys = tf.sin(x)\n",
    "ys = tf.reshape(ys, tf.pack([ksize, 1]))\n",
    "ones = tf.ones(tf.pack([1, ksize]))\n",
    "wave = tf.matmul(ys, ones)\n",
    "gabor = tf.mul(wave, z_2d)\n",
    "gabor_4d = tf.reshape(gabor, tf.pack([ksize, ksize, 1, 1]))\n",
    "\n",
    "# And finally, convolve the two:\n",
    "convolved = tf.nn.conv2d(img_4d, gabor_4d, strides=[1, 1, 1, 1], padding='SAME', name='convolved')\n",
    "convolved_img = convolved[0, :, :, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we've done is create an entire graph from our placeholders which is capable of convolving an image with a gabor kernel.  In order to compute it, we have to specify all of the placeholders required for its computation.\n",
    "\n",
    "If we try to evaluate it without specifying placeholders beforehand, we will get an error `InvalidArgumentError: You must feed a value for placeholder tensor 'img' with dtype float and shape [512,512]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\nCaused by op 'ksize', defined at:\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 171, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 86, in _run_code\n    exec(code, run_globals)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/traitlets/config/application.py\", line 596, in launch_instance\n    app.start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelapp.py\", line 442, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/ioloop.py\", line 162, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/ioloop.py\", line 883, in start\n    handler_func(fd_obj, events)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 391, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/ipkernel.py\", line 199, in do_execute\n    shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2705, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2809, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2869, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-45-4eb6dd4e6c0d>\", line 24, in <module>\n    ksize = tf.placeholder(tf.int32, name='ksize')\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py\", line 895, in placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 1238, in _placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py\", line 704, in apply_op\n    op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 2260, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 1230, in __init__\n    self._traceback = _extract_stack()\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 715\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    716\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m    696\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 697\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m    698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m                 \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/errors.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[0;34m()\u001b[0m\n\u001b[1;32m    449\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[1;32m    451\u001b[0m   \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-6bb9dbcbecae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mconvolved_img\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m     \"\"\"\n\u001b[0;32m--> 555\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_eval_using_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_eval_using_default_session\u001b[0;34m(tensors, feed_dict, graph, session)\u001b[0m\n\u001b[1;32m   3496\u001b[0m                        \u001b[0;34m\"the tensor's graph is different from the session's \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3497\u001b[0m                        \"graph.\")\n\u001b[0;32m-> 3498\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    370\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 372\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    373\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m       results = self._do_run(handle, target_list, unique_fetches,\n\u001b[0;32m--> 636\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    637\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m       \u001b[0;31m# The movers are no longer used. Delete them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    706\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m--> 708\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m    709\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\nCaused by op 'ksize', defined at:\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 171, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 86, in _run_code\n    exec(code, run_globals)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/traitlets/config/application.py\", line 596, in launch_instance\n    app.start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelapp.py\", line 442, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/ioloop.py\", line 162, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/ioloop.py\", line 883, in start\n    handler_func(fd_obj, events)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 391, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/ipkernel.py\", line 199, in do_execute\n    shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2705, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2809, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2869, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-45-4eb6dd4e6c0d>\", line 24, in <module>\n    ksize = tf.placeholder(tf.int32, name='ksize')\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py\", line 895, in placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 1238, in _placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py\", line 704, in apply_op\n    op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 2260, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 1230, in __init__\n    self._traceback = _extract_stack()\n"
     ]
    }
   ],
   "source": [
    "convolved_img.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's saying that we didn't specify our placeholder for `img`.  In order to \"feed a value\", we use the `feed_dict` parameter like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\nCaused by op 'ksize', defined at:\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 171, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 86, in _run_code\n    exec(code, run_globals)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/traitlets/config/application.py\", line 596, in launch_instance\n    app.start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelapp.py\", line 442, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/ioloop.py\", line 162, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/ioloop.py\", line 883, in start\n    handler_func(fd_obj, events)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 391, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/ipkernel.py\", line 199, in do_execute\n    shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2705, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2809, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2869, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-45-4eb6dd4e6c0d>\", line 24, in <module>\n    ksize = tf.placeholder(tf.int32, name='ksize')\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py\", line 895, in placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 1238, in _placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py\", line 704, in apply_op\n    op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 2260, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 1230, in __init__\n    self._traceback = _extract_stack()\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 715\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    716\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m    696\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 697\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m    698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m                 \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/errors.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[0;34m()\u001b[0m\n\u001b[1;32m    449\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[1;32m    451\u001b[0m   \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-15318c524e54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mconvolved_img\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcamera\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m     \"\"\"\n\u001b[0;32m--> 555\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_eval_using_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_eval_using_default_session\u001b[0;34m(tensors, feed_dict, graph, session)\u001b[0m\n\u001b[1;32m   3496\u001b[0m                        \u001b[0;34m\"the tensor's graph is different from the session's \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3497\u001b[0m                        \"graph.\")\n\u001b[0;32m-> 3498\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    370\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 372\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    373\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m       results = self._do_run(handle, target_list, unique_fetches,\n\u001b[0;32m--> 636\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    637\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m       \u001b[0;31m# The movers are no longer used. Delete them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    706\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m--> 708\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m    709\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\nCaused by op 'ksize', defined at:\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 171, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 86, in _run_code\n    exec(code, run_globals)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/traitlets/config/application.py\", line 596, in launch_instance\n    app.start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelapp.py\", line 442, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/ioloop.py\", line 162, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/ioloop.py\", line 883, in start\n    handler_func(fd_obj, events)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 391, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/ipkernel.py\", line 199, in do_execute\n    shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2705, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2809, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2869, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-45-4eb6dd4e6c0d>\", line 24, in <module>\n    ksize = tf.placeholder(tf.int32, name='ksize')\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py\", line 895, in placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 1238, in _placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py\", line 704, in apply_op\n    op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 2260, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 1230, in __init__\n    self._traceback = _extract_stack()\n"
     ]
    }
   ],
   "source": [
    "convolved_img.eval(feed_dict={img: data.camera()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But that's not the only placeholder in our graph!  We also have placeholders for `mean`, `sigma`, and `ksize`.  Once we specify all of them, we'll have our result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1168f7f28>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aUlSeekoZZPIgRVFkVR29qJRUJV4EoqRRrVbl85//vOzYsUOWl5flq1/9quze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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116637128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = convolved_img.eval(feed_dict={\n",
    "    img: data.camera(), mean:0.0, sigma:1.0, ksize:100})\n",
    "plt.imshow(res, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, instead of having to rewrite the entire graph, we can just specify the different placeholders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x116bea5f8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7xnNTrntdG5bMR7ep+Owwvue+Z5wXSQ2upK0CDVvnmUQ4SbqY6CIb0iedOLay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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116908780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = convolved_img.eval(feed_dict={\n",
    "    img: data.camera(),\n",
    "    mean: 0.0,\n",
    "    sigma: 0.5,\n",
    "    ksize: 32\n",
    "  })\n",
    "plt.imshow(res, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"homework\"></a>\n",
    "# Homework\n",
    "\n",
    "For your first assignment, we'll work on creating our own dataset.  You'll need to find at least 100 images and work through the [notebook](session-1.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"next-session\"></a>\n",
    "# Next Session\n",
    "\n",
    "In the next session, we'll create our first Neural Network and see how it can be used to paint an image.\n",
    "\n",
    "<a name=\"reading-material\"></a>\n",
    "# Reading Material\n",
    "\n",
    "Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., … Zheng, X. (2015). TensorFlow : Large-Scale Machine Learning on Heterogeneous Distributed Systems.\n",
    "https://arxiv.org/abs/1603.04467\n",
    "\n",
    "Yoshua Bengio, Aaron Courville, Pascal Vincent.  Representation Learning: A Review and New Perspectives.  24 Jun 2012.\n",
    "https://arxiv.org/abs/1206.5538\n",
    "\n",
    "J. Schmidhuber. Deep Learning in Neural Networks: An Overview. Neural Networks, 61, p 85-117, 2015.\n",
    "https://arxiv.org/abs/1404.7828\n",
    "\n",
    "LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521, no. 7553 (2015): 436-444.\n",
    "\n",
    "Ian Goodfellow Yoshua Bengio and Aaron Courville.  Deep Learning.  2016.\n",
    "http://www.deeplearningbook.org/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
